Persistent spectral theory-guided protein engineering
This paper introduces TopFit, a framework that combines persistent spectral theory and sequence embeddings to predict protein fitness from mutations, validated on 34 benchmark datasets.
Author
University of California, IrvineORCID 0000-0002-7058-6428Unclaimed ORCID profile · follow to get notified of new preprints
3 papers
Recent·Most cited
This paper introduces TopFit, a framework that combines persistent spectral theory and sequence embeddings to predict protein fitness from mutations, validated on 34 benchmark datasets.
Yuchi Qiu, Lianna Fung, Thomas Friedrich Schilling, Qing Hua Nie
The paper develops a stochastic multiscale cell-based model to study how multiple morphogens and tissue dynamics regulate segment formation in the zebrafish hindbrain.
Yilun Zhu, Yuchi Qiu, Weitao Chen…·Arthur D. Lander
The paper investigates how Dpp morphogen gradients scale in Drosophila wing discs and proposes an alternative feedback model to explain scaling failure in mutants.